CSC321 Lecture 1: Introduction Roger Grosse Roger Grosse CSC321 - PowerPoint PPT Presentation

CSC321 Lecture 1: Introduction Roger Grosse Roger Grosse CSC321 Lecture 1: Introduction 1 / 29

What is machine learning? For many problems, it’s difficult to program the correct behavior by hand recognizing people and objects understanding human speech Roger Grosse CSC321 Lecture 1: Introduction 2 / 29

What is machine learning? For many problems, it’s difficult to program the correct behavior by hand recognizing people and objects understanding human speech Machine learning approach: program an algorithm to automatically learn from data, or from experience Roger Grosse CSC321 Lecture 1: Introduction 2 / 29

What is machine learning? For many problems, it’s difficult to program the correct behavior by hand recognizing people and objects understanding human speech Machine learning approach: program an algorithm to automatically learn from data, or from experience Some reasons you might want to use a learning algorithm: hard to code up a solution by hand (e.g. vision, speech) system needs to adapt to a changing environment (e.g. spam detection) want the system to perform better than the human programmers privacy/fairness (e.g. ranking search results) Roger Grosse CSC321 Lecture 1: Introduction 2 / 29

What is machine learning? It’s similar to statistics... Both fields try to uncover patterns in data Both fields draw heavily on calculus, probability, and linear algebra, and share many of the same core algorithms Roger Grosse CSC321 Lecture 1: Introduction 3 / 29

What is machine learning? It’s similar to statistics... Both fields try to uncover patterns in data Both fields draw heavily on calculus, probability, and linear algebra, and share many of the same core algorithms But it’s not statistics! Stats is more concerned with helping scientists and policymakers draw good conclusions; ML is more concerned with building autonomous agents Stats puts more emphasis on interpretability and mathematical rigor; ML puts more emphasis on predictive performance, scalability, and autonomy Roger Grosse CSC321 Lecture 1: Introduction 3 / 29

What is machine learning? Types of machine learning Supervised learning: have labeled examples of the correct behavior Reinforcement learning: learning system receives a reward signal, tries to learn to maximize the reward signal Unsupervised learning: no labeled examples – instead, looking for interesting patterns in the data Roger Grosse CSC321 Lecture 1: Introduction 4 / 29

Course information Course about machine learning, with a focus on neural networks Independent of CSC411, and CSC412, with about 25% overlap in topics First 2/3: supervised learning Last 1/3: unsupervised learning Maybe a bit of reinforcement learning, time permitting Two sections Equivalent content, same assignments and exams Both sections are full, so please attend your own. Roger Grosse CSC321 Lecture 1: Introduction 5 / 29

Course information Formal prerequisites: Calculus: (MAT136H1 with a minimum mark of 77)/(MAT137Y1 with a minimum mark of 73)/(MAT157Y1 with a minimum mark of 67)/MAT235Y1/MAT237Y1/MAT257Y1 Linear Algebra: MAT221H1/MAT223H1/MAT240H1 Probability: STA247H1/STA255H1/STA257H1 Multivariable calculus (recommended): MAT235Y1/MAT237Y1/MAT257Y1 Programming experience (recommended) Roger Grosse CSC321 Lecture 1: Introduction 6 / 29

Course information Expectations and marking Weekly homeworks (10% of total mark) Due Monday nights at 11:59pm, starting 1/16 2-3 short conceptual questions Use material covered up through Tuesday of the preceding week 4 programming assignments (10% each) Python 10-15 lines of code may also involve some mathematical derivations give you a chance to experiment with the algorithms Exams midterm (15%) final (35%) See Course Information handout for detailed policies Roger Grosse CSC321 Lecture 1: Introduction 7 / 29

Course information Textbooks None, but we link to lots of free online resources. (see syllabus) Professor Geoffrey Hinton’s Coursera lectures the Deep Learning textbook by Goodfellow et al. Metacademy I will try to post detailed lecture notes, but I will not have time to cover every lecture. Tutorials Roughly every week Programming background; worked-through examples Roger Grosse CSC321 Lecture 1: Introduction 8 / 29

Course information Course web page: http://www.cs.toronto.edu/~rgrosse/courses/csc321_2017/ Includes detailed course information handout Roger Grosse CSC321 Lecture 1: Introduction 9 / 29

Supervised learning examples Supervised learning: have labeled examples of the correct behavior e.g. Handwritten digit classification with the MNIST dataset Task: given an image of a handwritten digit, predict the digit class Input: the image Target: the digit class Roger Grosse CSC321 Lecture 1: Introduction 10 / 29

Supervised learning examples Supervised learning: have labeled examples of the correct behavior e.g. Handwritten digit classification with the MNIST dataset Task: given an image of a handwritten digit, predict the digit class Input: the image Target: the digit class Data: 70,000 images of handwritten digits labeled by humans Training set: first 60,000 images, used to train the network Test set: last 10,000 images, not available during training, used to evaluate performance Roger Grosse CSC321 Lecture 1: Introduction 10 / 29

Supervised learning examples Supervised learning: have labeled examples of the correct behavior e.g. Handwritten digit classification with the MNIST dataset Task: given an image of a handwritten digit, predict the digit class Input: the image Target: the digit class Data: 70,000 images of handwritten digits labeled by humans Training set: first 60,000 images, used to train the network Test set: last 10,000 images, not available during training, used to evaluate performance This dataset is the “fruit fly” of neural net research Current best algorithm has only 0.23% error rate on the test set! Roger Grosse CSC321 Lecture 1: Introduction 10 / 29

Supervised learning examples What makes a “2”? Roger Grosse CSC321 Lecture 1: Introduction 11 / 29

Supervised learning examples Object recognition (Krizhevsky and Hinton, 2012) ImageNet dataset: thousands of categories, millions of labeled images Lots of variability in viewpoint, lighting, etc. Error rate dropped from 25.7% to 5.7% over the course of a few years! Roger Grosse CSC321 Lecture 1: Introduction 12 / 29

Supervised learning examples Caption generation Given: dataset of Flickr images with captions More examples at http://deeplearning.cs.toronto.edu/i2t Roger Grosse CSC321 Lecture 1: Introduction 13 / 29

Unsupervised learning examples Unsupervised learning: no labeled examples – instead, looking for interesting patterns in the data E.g. visualization of documents; algorithm was given 800,000 newswire stories, and learned to represent these documents as points in two-dimensional space Colors are based on human labels, but these weren’t given to the algorithm Roger Grosse CSC321 Lecture 1: Introduction 14 / 29

Unsupervised learning examples Automatic mouse tracking When biologists do behavioral genetics researchers on mice, it’s very time consuming for a person to sit and label everything a mouse does The Datta lab at Harvard is building a system for automatically tracking mouse behaviors Goal: show the researchers a summary of how much time different mice spend on various behaviors, so they can determine the effects of the genetic manipulations One of the major challenges is that we don’t know the right “vocabulary” for describing the behaviors — clustering the observations into meaningful groups is an unsupervised learning task video: http://www.sciencedirect.com/science/article/pii/ S0896627315010375 Roger Grosse CSC321 Lecture 1: Introduction 15 / 29

Reinforcement learning An agent interacts with an environment (e.g. game of Breakout) In each time step, the agent receives observations (e.g. pixels) which give it information about the state (e.g. positions of the ball and paddle) the agent picks an action (e.g. keystrokes) which affects the state The agent periodically receives a reward (e.g. points) The agent wants to learn a policy , or mapping from observations to actions, which maximizes its average reward over time Roger Grosse CSC321 Lecture 1: Introduction 16 / 29

Reinforcement learning DeepMind trained neural networks to play many different Atari games given the raw screen as input, plus the score as a reward single network architecture shared between all the games in many cases, the networks learned to play better than humans (in terms of points in the first minute) https://www.youtube.com/watch?v=V1eYniJ0Rnk Roger Grosse CSC321 Lecture 1: Introduction 17 / 29

What are neural networks? Most of the biological details aren’t essential, so we use vastly simplified models of neurons. While neural nets originally drew inspiration from the brain, nowadays we mostly think about math, statistics, etc. y i'th weight bias output output � � w 1 w 2 weights � w 3 y = g b + x i w i inputs i x 1 x 2 x 3 nonlinearity i'th input Neural networks are collections of thousands (or millions) of these simple processing units that together perform useful computations. Roger Grosse CSC321 Lecture 1: Introduction 18 / 29